Various techniques have been developed for acquiring and processing discrete pixel image data. Discrete pixel images are composed of an array or matrix of pixels having varying properties, such as intensity, color and so on. The data defining each pixel may be acquired in various manners, depending upon the imaging modality employed. Modalities in medical imaging, for example, include magnetic resonance imaging techniques, X-ray techniques, and so forth. In general, however, each pixel is represented by a signal, typically a digitized value representative of a sensed parameter, such as an emission of material excited within each pixel region or radiation received within each pixel region. To facilitate interpretation of the image, the pixel values must be filtered and processed to enhance definition of features of interest to an observer. Ultimately, the processed image is reconstituted for display or printing.
Various techniques have been employed for enhancing discrete pixel images to facilitate their interpretation. Such techniques may employ identification of contrast regions, edges, and so forth, as defined by series of pixels or groups of pixels within the image matrix. Smoothing and sharpening steps may be employed for bringing out certain edges or contrast regions, or for de-emphasizing specific areas not considered of interest.
However, heretofore known techniques do not always provide a satisfactory degree of flexibility or computational efficiency. In particular, existing techniques may require operator intervention in defining salient structures, sometimes requiring processing of raw data several times based on operator adjustments before arriving at an acceptable final image. Moreover, where variations in image signal acquisition, processing and display circuitry between systems and between images in a single system result in corresponding variations in relationships between the pixels defining an image, structures of interest within a subject may not be consistently sensed, processed and displayed. Consequently, structures, textures, contrasts and other image features may be difficult to visualize and compare both within single images and between a set of images. As a result, attending physicians or radiologists presented with the images may experience difficulties in interpreting the relevant structures.